diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py index f164375bf7..4bc6064b6c 100644 --- a/src/diffusers/loaders/single_file_utils.py +++ b/src/diffusers/loaders/single_file_utils.py @@ -83,25 +83,29 @@ SCHEDULER_DEFAULT_CONFIG = { DIFFUSERS_TO_LDM_MAPPING = { "unet": { - "time_embedding.linear_1.weight": "time_embed.0.weight", - "time_embedding.linear_1.bias": "time_embed.0.bias", - "time_embedding.linear_2.weight": "time_embed.2.weight", - "time_embedding.linear_2.bias": "time_embed.2.bias", - "conv_in.weight": "input_blocks.0.0.weight", - "conv_in.bias": "input_blocks.0.0.bias", + "layers": { + "time_embedding.linear_1.weight": "time_embed.0.weight", + "time_embedding.linear_1.bias": "time_embed.0.bias", + "time_embedding.linear_2.weight": "time_embed.2.weight", + "time_embedding.linear_2.bias": "time_embed.2.bias", + "conv_in.weight": "input_blocks.0.0.weight", + "conv_in.bias": "input_blocks.0.0.bias", + "conv_norm_out.weight": "out.0.weight", + "conv_norm_out.bias": "out.0.bias", + "conv_out.weight": "out.2.weight", + "conv_out.bias": "out.2.bias", + }, "class_embed_type": { - "timestep": { - "class_embedding.linear_1.weight": "label_emb.0.0.weight", - "class_embedding.linear_1.bias": "label_emb.0.0.bias", - "class_embedding.linear_2.weight": "label_emb.0.2.weight", - "class_embedding.linear_2.bias": "label_emb.0.2.bias", - }, - "text_time": { - "class_embedding.linear_1.weight": "label_emb.0.0.weight", - "class_embedding.linear_1.bias": "label_emb.0.0.bias", - "class_embedding.linear_2.weight": "label_emb.0.2.weight", - "class_embedding.linear_2.bias": "label_emb.0.2.bias", - }, + "class_embedding.linear_1.weight": "label_emb.0.0.weight", + "class_embedding.linear_1.bias": "label_emb.0.0.bias", + "class_embedding.linear_2.weight": "label_emb.0.2.weight", + "class_embedding.linear_2.bias": "label_emb.0.2.bias", + }, + "addition_embed_type": { + "add_embedding.linear_1.weight": "label_emb.0.0.weight", + "add_embedding.linear_1.bias": "label_emb.0.0.bias", + "add_embedding.linear_2.weight": "label_emb.0.2.weight", + "add_embedding.linear_2.bias": "label_emb.0.2.bias", }, }, "vae": { @@ -586,6 +590,27 @@ def create_vae_diffusers_config(original_config, image_size: int): return config +def update_unet_resnet_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping=None): + for ldm_key in ldm_keys: + diffusers_key = ( + ldm_key.replace("in_layers.0", "norm1") + .replace("in_layers.2", "conv1") + .replace("out_layers.0", "norm2") + .replace("out_layers.3", "conv2") + .replace("emb_layers.1", "time_emb_proj") + .replace("skip_connection", "conv_shortcut") + ) + if mapping: + diffusers_key = diffusers_key.replace(mapping["old"], mapping["new"]) + new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key) + + +def update_unet_attention_ldm_to_diffusers(ldm_keys, new_checkpoint, checkpoint, mapping): + for ldm_key in ldm_keys: + diffusers_key = ldm_key.replace(mapping["old"], mapping["new"]) + new_checkpoint[diffusers_key] = checkpoint.pop(ldm_key) + + def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False, skip_extract_state_dict=False): """ Takes a state dict and a config, and returns a converted checkpoint. @@ -598,7 +623,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False unet_state_dict = {} keys = list(checkpoint.keys()) - unet_key = "model.diffusion_model." + unet_key = LDM_UNET_KEY # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: @@ -623,41 +648,25 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) new_checkpoint = {} + ldm_unet_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["layers"] + for diffusers_key, ldm_key in ldm_unet_keys.items(): + new_checkpoint[diffusers_key] = unet_state_dict[ldm_key] - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + if config["class_embed_type"] in ["timestep", "projection"]: + class_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["class_embed_type"] + for diffusers_key, ldm_key in class_embed_keys.items(): + new_checkpoint[diffusers_key] = unet_state_dict[ldm_key] if config["addition_embed_type"] == "text_time": - new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + addition_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["addition_embed_type"] + for diffusers_key, ldm_key in addition_embed_keys.items(): + new_checkpoint[diffusers_key] = unet_state_dict[ldm_key] # Relevant to StableDiffusionUpscalePipeline if "num_class_embeds" in config: if (config["num_class_embeds"] is not None) and ("label_emb.weight" in unet_state_dict): new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"] - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] # Retrieves the keys for the input blocks only num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) input_blocks = { @@ -679,6 +688,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False for layer_id in range(num_output_blocks) } + # Down blocks for i in range(1, num_input_blocks): block_id = (i - 1) // (config["layers_per_block"] + 1) layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) @@ -686,7 +696,12 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False resnets = [ key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + update_unet_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + unet_state_dict, + {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}, + ) if f"input_blocks.{i}.0.op.weight" in unet_state_dict: new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( @@ -696,48 +711,77 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False f"input_blocks.{i}.0.op.bias" ) - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + if attentions: + update_unet_attention_ldm_to_diffusers( + attentions, + new_checkpoint, + unet_state_dict, + {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}, ) + # Mid blocks resnet_0 = middle_blocks[0] attentions = middle_blocks[1] resnet_1 = middle_blocks[2] - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + update_unet_resnet_ldm_to_diffusers( + resnet_0, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.0", "new": "mid_block.resnets.0"} + ) + update_unet_resnet_ldm_to_diffusers( + resnet_1, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.2", "new": "mid_block.resnets.1"} + ) + update_unet_attention_ldm_to_diffusers( + attentions, new_checkpoint, unet_state_dict, mapping={"old": "middle_block.1", "new": "mid_block.attentions.0"} ) + # Up Blocks for i in range(num_output_blocks): block_id = i // (config["layers_per_block"] + 1) layer_in_block_id = i % (config["layers_per_block"] + 1) + + resnets = [ + key for key in output_blocks[i] if f"output_blocks.{i}.0" in key and f"output_blocks.{i}.0.op" not in key + ] + update_unet_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + unet_state_dict, + {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}, + ) + + attentions = [key for key in input_blocks[i] if f"output_blocks.{i}.1" in key] + if attentions: + update_unet_attention_ldm_to_diffusers( + attentions, + new_checkpoint, + unet_state_dict, + {"old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}"}, + ) + + if f"output_blocks.{i}.1.conv.weight" in unet_state_dict: + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.1.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.1.conv.bias" + ] + if f"output_blocks.{i}.2.conv.weight" in unet_state_dict: + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.2.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.2.conv.bias" + ] + + """ output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] output_block_list = {} for layer in output_block_layers: layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] + output_block_list.setdefault(layer_id, []) + output_block_list[layer_id].append(layer_name) if len(output_block_list) > 1: resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] @@ -781,6 +825,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) new_checkpoint[new_path] = unet_state_dict[old_path] + """ return new_checkpoint